Pattern recognition methods and applications in biomedical magnetic resonance
Multivariate statistical methods, sometimes termed as chemometrics or bio-informatics, have been used to extract information from high resolution nuclear magnetic resonance (NMR) spectra of biological samples. A promising approach for releasing information in such complex data sets lies in the power...
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| Veröffentlicht in: | Progress in nuclear magnetic resonance spectroscopy 2001-07, Vol.39 (1), p.1-40 |
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| container_title | Progress in nuclear magnetic resonance spectroscopy |
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| creator | Lindon, J.C. Holmes, E. Nicholson, J.K. |
| description | Multivariate statistical methods, sometimes termed as chemometrics or bio-informatics, have been used to extract information from high resolution nuclear magnetic resonance (NMR) spectra of biological samples. A promising approach for releasing information in such complex data sets lies in the power of computer pattern recognition algorithms. Thus, the combination of NMR spectroscopy with pattern recognition methods has the potential for generating relevant information and production of a significant knowledge base in many areas including understanding disease processes, assessing the effectiveness of therapies, and evaluating the side effects of drugs. |
| doi_str_mv | 10.1016/S0079-6565(00)00036-4 |
| format | Article |
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| ispartof | Progress in nuclear magnetic resonance spectroscopy, 2001-07, Vol.39 (1), p.1-40 |
| issn | 0079-6565 |
| language | eng |
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| source | Access via ScienceDirect (Elsevier) |
| subjects | Algorithms Knowledge based systems Medical computing Medical imaging Pattern recognition Statistical methods |
| title | Pattern recognition methods and applications in biomedical magnetic resonance |
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